Overview

Dataset statistics

Number of variables35
Number of observations19219
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 MiB
Average record size in memory280.0 B

Variable types

Numeric25
Categorical10

Alerts

Edges_X_Index is highly overall correlated with LogOfAreas and 6 other fieldsHigh correlation
Edges_Y_Index is highly overall correlated with Empty_Index and 10 other fieldsHigh correlation
Empty_Index is highly overall correlated with Edges_Y_Index and 3 other fieldsHigh correlation
K_Scatch is highly overall correlated with Edges_Y_Index and 10 other fieldsHigh correlation
LogOfAreas is highly overall correlated with Edges_X_Index and 12 other fieldsHigh correlation
Log_X_Index is highly overall correlated with Edges_Y_Index and 10 other fieldsHigh correlation
Log_Y_Index is highly overall correlated with Edges_X_Index and 10 other fieldsHigh correlation
Luminosity_Index is highly overall correlated with Maximum_of_Luminosity and 1 other fieldsHigh correlation
Maximum_of_Luminosity is highly overall correlated with Luminosity_IndexHigh correlation
Minimum_of_Luminosity is highly overall correlated with K_Scatch and 3 other fieldsHigh correlation
Orientation_Index is highly overall correlated with Edges_Y_Index and 1 other fieldsHigh correlation
Outside_Global_Index is highly overall correlated with Orientation_IndexHigh correlation
Outside_X_Index is highly overall correlated with Edges_Y_Index and 10 other fieldsHigh correlation
Pixels_Areas is highly overall correlated with Edges_X_Index and 11 other fieldsHigh correlation
SigmoidOfAreas is highly overall correlated with Edges_X_Index and 10 other fieldsHigh correlation
Stains is highly overall correlated with LogOfAreas and 1 other fieldsHigh correlation
Steel_Plate_Thickness is highly overall correlated with TypeOfSteel_A300 and 1 other fieldsHigh correlation
Sum_of_Luminosity is highly overall correlated with Edges_X_Index and 10 other fieldsHigh correlation
TypeOfSteel_A300 is highly overall correlated with Steel_Plate_Thickness and 1 other fieldsHigh correlation
TypeOfSteel_A400 is highly overall correlated with Steel_Plate_Thickness and 1 other fieldsHigh correlation
X_Maximum is highly overall correlated with X_MinimumHigh correlation
X_Minimum is highly overall correlated with X_MaximumHigh correlation
X_Perimeter is highly overall correlated with Edges_X_Index and 11 other fieldsHigh correlation
Y_Maximum is highly overall correlated with Y_MinimumHigh correlation
Y_Minimum is highly overall correlated with Y_MaximumHigh correlation
Y_Perimeter is highly overall correlated with Edges_X_Index and 10 other fieldsHigh correlation
Pastry is highly imbalanced (61.1%)Imbalance
Z_Scratch is highly imbalanced (67.3%)Imbalance
Stains is highly imbalanced (80.8%)Imbalance
Dirtiness is highly imbalanced (83.0%)Imbalance
id is uniformly distributedUniform
id has unique valuesUnique
X_Minimum has 326 (1.7%) zerosZeros
Edges_Index has 447 (2.3%) zerosZeros
Orientation_Index has 735 (3.8%) zerosZeros

Reproduction

Analysis started2024-03-08 17:00:50.124213
Analysis finished2024-03-08 17:02:34.377181
Duration1 minute and 44.25 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct19219
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9609
Minimum0
Maximum19218
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:34.547949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile960.9
Q14804.5
median9609
Q314413.5
95-th percentile18257.1
Maximum19218
Range19218
Interquartile range (IQR)9609

Descriptive statistics

Standard deviation5548.1917
Coefficient of variation (CV)0.57739533
Kurtosis-1.2
Mean9609
Median Absolute Deviation (MAD)4805
Skewness0
Sum1.8467537 × 108
Variance30782432
MonotonicityStrictly increasing
2024-03-08T09:02:34.764417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
12819 1
 
< 0.1%
12817 1
 
< 0.1%
12816 1
 
< 0.1%
12815 1
 
< 0.1%
12814 1
 
< 0.1%
12813 1
 
< 0.1%
12812 1
 
< 0.1%
12811 1
 
< 0.1%
12810 1
 
< 0.1%
Other values (19209) 19209
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
19218 1
< 0.1%
19217 1
< 0.1%
19216 1
< 0.1%
19215 1
< 0.1%
19214 1
< 0.1%
19213 1
< 0.1%
19212 1
< 0.1%
19211 1
< 0.1%
19210 1
< 0.1%
19209 1
< 0.1%

X_Minimum
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1191
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean709.85468
Minimum0
Maximum1705
Zeros326
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:34.970818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q149
median777
Q31152
95-th percentile1579
Maximum1705
Range1705
Interquartile range (IQR)1103

Descriptive statistics

Standard deviation531.54419
Coefficient of variation (CV)0.74880705
Kurtosis-1.3276815
Mean709.85468
Median Absolute Deviation (MAD)474
Skewness0.016978784
Sum13642697
Variance282539.22
MonotonicityNot monotonic
2024-03-08T09:02:35.179882image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 1601
 
8.3%
41 1489
 
7.7%
0 326
 
1.7%
1325 155
 
0.8%
1065 148
 
0.8%
621 133
 
0.7%
9 108
 
0.6%
1245 106
 
0.6%
853 94
 
0.5%
13 92
 
0.5%
Other values (1181) 14967
77.9%
ValueCountFrequency (%)
0 326
1.7%
1 36
 
0.2%
2 59
 
0.3%
3 15
 
0.1%
4 63
 
0.3%
5 56
 
0.3%
6 54
 
0.3%
7 31
 
0.2%
8 25
 
0.1%
9 108
 
0.6%
ValueCountFrequency (%)
1705 3
 
< 0.1%
1695 1
 
< 0.1%
1689 2
 
< 0.1%
1688 11
0.1%
1687 23
0.1%
1683 6
 
< 0.1%
1682 8
 
< 0.1%
1680 3
 
< 0.1%
1679 1
 
< 0.1%
1678 14
0.1%

X_Maximum
Real number (ℝ)

HIGH CORRELATION 

Distinct1259
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean753.85764
Minimum4
Maximum1713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:35.384977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile27
Q1214
median796
Q31165
95-th percentile1592
Maximum1713
Range1709
Interquartile range (IQR)951

Descriptive statistics

Standard deviation499.8366
Coefficient of variation (CV)0.66303845
Kurtosis-1.2571896
Mean753.85764
Median Absolute Deviation (MAD)466
Skewness0.072164898
Sum14488390
Variance249836.63
MonotonicityNot monotonic
2024-03-08T09:02:35.592501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
214 376
 
2.0%
216 374
 
1.9%
212 360
 
1.9%
218 232
 
1.2%
1332 177
 
0.9%
192 161
 
0.8%
194 160
 
0.8%
191 154
 
0.8%
211 129
 
0.7%
1084 127
 
0.7%
Other values (1249) 16969
88.3%
ValueCountFrequency (%)
4 6
 
< 0.1%
5 5
 
< 0.1%
6 4
 
< 0.1%
8 20
 
0.1%
9 31
 
0.2%
10 31
 
0.2%
11 22
 
0.1%
12 76
0.4%
13 67
0.3%
14 78
0.4%
ValueCountFrequency (%)
1713 4
 
< 0.1%
1712 1
 
< 0.1%
1711 1
 
< 0.1%
1698 2
 
< 0.1%
1696 14
0.1%
1695 1
 
< 0.1%
1694 20
0.1%
1692 4
 
< 0.1%
1690 6
 
< 0.1%
1689 6
 
< 0.1%

Y_Minimum
Real number (ℝ)

HIGH CORRELATION 

Distinct3345
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1849756
Minimum6712
Maximum12987661
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:35.805723image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6712
5-th percentile96812
Q1657468
median1398169
Q32368032
95-th percentile5367324
Maximum12987661
Range12980949
Interquartile range (IQR)1710564

Descriptive statistics

Standard deviation1903553.9
Coefficient of variation (CV)1.0290837
Kurtosis8.8679561
Mean1849756
Median Absolute Deviation (MAD)826646
Skewness2.5788578
Sum3.5550461 × 1010
Variance3.6235173 × 1012
MonotonicityNot monotonic
2024-03-08T09:02:36.019810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1740121 53
 
0.3%
879259 50
 
0.3%
1786253 47
 
0.2%
5851552 43
 
0.2%
7754878 43
 
0.2%
1647908 42
 
0.2%
2887095 41
 
0.2%
2488529 41
 
0.2%
1084661 40
 
0.2%
2886302 40
 
0.2%
Other values (3335) 18779
97.7%
ValueCountFrequency (%)
6712 4
 
< 0.1%
7003 19
0.1%
7430 33
0.2%
7851 4
 
< 0.1%
9007 5
 
< 0.1%
9228 7
 
< 0.1%
9246 3
 
< 0.1%
12799 8
 
< 0.1%
13302 2
 
< 0.1%
13320 2
 
< 0.1%
ValueCountFrequency (%)
12987661 30
0.2%
12920561 1
 
< 0.1%
12917033 3
 
< 0.1%
12806495 8
 
< 0.1%
12725281 9
 
< 0.1%
12577343 1
 
< 0.1%
12438460 4
 
< 0.1%
12425281 1
 
< 0.1%
12416489 1
 
< 0.1%
12416454 2
 
< 0.1%

Y_Maximum
Real number (ℝ)

HIGH CORRELATION 

Distinct3341
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1846605.3
Minimum6724
Maximum12987692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:36.225910image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6724
5-th percentile97109.8
Q1657502
median1398179
Q32362511
95-th percentile5367377
Maximum12987692
Range12980968
Interquartile range (IQR)1705009

Descriptive statistics

Standard deviation1896295.1
Coefficient of variation (CV)1.0269087
Kurtosis8.8421079
Mean1846605.3
Median Absolute Deviation (MAD)826641
Skewness2.5719886
Sum3.5489908 × 1010
Variance3.5959353 × 1012
MonotonicityNot monotonic
2024-03-08T09:02:36.443923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1740188 53
 
0.3%
879265 50
 
0.3%
1786320 47
 
0.2%
2887114 44
 
0.2%
7754931 42
 
0.2%
5851560 42
 
0.2%
909492 40
 
0.2%
2488541 40
 
0.2%
618502 40
 
0.2%
2886367 38
 
0.2%
Other values (3331) 18783
97.7%
ValueCountFrequency (%)
6724 4
 
< 0.1%
7020 20
0.1%
7458 32
0.2%
7865 4
 
< 0.1%
9033 5
 
< 0.1%
9228 1
 
< 0.1%
9246 6
 
< 0.1%
9253 1
 
< 0.1%
12804 8
 
< 0.1%
13320 12
 
0.1%
ValueCountFrequency (%)
12987692 30
0.2%
12920662 1
 
< 0.1%
12917094 2
 
< 0.1%
12917040 1
 
< 0.1%
12806520 8
 
< 0.1%
12725314 9
 
< 0.1%
12577396 1
 
< 0.1%
12438491 2
 
< 0.1%
12425575 1
 
< 0.1%
12416473 2
 
< 0.1%

Pixels_Areas
Real number (ℝ)

HIGH CORRELATION 

Distinct1154
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1683.9876
Minimum6
Maximum152655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:36.829271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile51
Q189
median168
Q3653
95-th percentile9596.1
Maximum152655
Range152649
Interquartile range (IQR)564

Descriptive statistics

Standard deviation3730.3199
Coefficient of variation (CV)2.2151706
Kurtosis181.7372
Mean1683.9876
Median Absolute Deviation (MAD)102
Skewness6.9785633
Sum32364558
Variance13915286
MonotonicityNot monotonic
2024-03-08T09:02:37.045357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
52 260
 
1.4%
114 223
 
1.2%
101 215
 
1.1%
55 203
 
1.1%
108 181
 
0.9%
74 180
 
0.9%
184 175
 
0.9%
110 168
 
0.9%
170 168
 
0.9%
140 165
 
0.9%
Other values (1144) 17281
89.9%
ValueCountFrequency (%)
6 26
 
0.1%
8 3
 
< 0.1%
9 3
 
< 0.1%
10 4
 
< 0.1%
11 6
 
< 0.1%
12 97
0.5%
13 1
 
< 0.1%
14 1
 
< 0.1%
15 11
 
0.1%
16 150
0.8%
ValueCountFrequency (%)
152655 1
 
< 0.1%
109075 1
 
< 0.1%
38376 1
 
< 0.1%
37376 1
 
< 0.1%
37334 1
 
< 0.1%
25906 1
 
< 0.1%
25473 3
< 0.1%
25323 2
 
< 0.1%
24365 3
< 0.1%
22554 5
< 0.1%

X_Perimeter
Real number (ℝ)

HIGH CORRELATION 

Distinct460
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.654665
Minimum2
Maximum7553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:37.260000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9
Q115
median25
Q364
95-th percentile578
Maximum7553
Range7551
Interquartile range (IQR)49

Descriptive statistics

Standard deviation177.82138
Coefficient of variation (CV)1.8589933
Kurtosis167.40848
Mean95.654665
Median Absolute Deviation (MAD)13
Skewness6.3180969
Sum1838387
Variance31620.444
MonotonicityNot monotonic
2024-03-08T09:02:37.466450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 867
 
4.5%
12 859
 
4.5%
14 738
 
3.8%
13 704
 
3.7%
18 623
 
3.2%
10 587
 
3.1%
20 549
 
2.9%
11 519
 
2.7%
16 511
 
2.7%
9 498
 
2.6%
Other values (450) 12764
66.4%
ValueCountFrequency (%)
2 2
 
< 0.1%
3 9
 
< 0.1%
4 42
 
0.2%
5 98
 
0.5%
6 181
 
0.9%
7 137
 
0.7%
8 275
1.4%
9 498
2.6%
10 587
3.1%
11 519
2.7%
ValueCountFrequency (%)
7553 1
 
< 0.1%
1193 3
< 0.1%
1169 3
< 0.1%
1139 1
 
< 0.1%
1138 4
< 0.1%
1089 1
 
< 0.1%
1084 2
 
< 0.1%
1050 2
 
< 0.1%
1029 1
 
< 0.1%
1022 6
< 0.1%

Y_Perimeter
Real number (ℝ)

HIGH CORRELATION 

Distinct331
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.124096
Minimum1
Maximum903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:37.678382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q114
median23
Q361
95-th percentile357
Maximum903
Range902
Interquartile range (IQR)47

Descriptive statistics

Standard deviation101.05418
Coefficient of variation (CV)1.5759158
Kurtosis9.2920044
Mean64.124096
Median Absolute Deviation (MAD)12
Skewness2.9774109
Sum1232401
Variance10211.947
MonotonicityNot monotonic
2024-03-08T09:02:37.872015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 865
 
4.5%
12 854
 
4.4%
10 689
 
3.6%
14 658
 
3.4%
15 594
 
3.1%
13 588
 
3.1%
17 570
 
3.0%
20 557
 
2.9%
18 490
 
2.5%
8 442
 
2.3%
Other values (321) 12912
67.2%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 5
 
< 0.1%
3 45
 
0.2%
4 346
1.8%
5 155
 
0.8%
6 190
 
1.0%
7 299
1.6%
8 442
2.3%
9 307
1.6%
10 689
3.6%
ValueCountFrequency (%)
903 1
 
< 0.1%
748 1
 
< 0.1%
712 2
 
< 0.1%
709 5
< 0.1%
696 1
 
< 0.1%
684 3
< 0.1%
680 3
< 0.1%
605 2
 
< 0.1%
604 3
< 0.1%
597 5
< 0.1%

Sum_of_Luminosity
Real number (ℝ)

HIGH CORRELATION 

Distinct2595
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean191846.68
Minimum250
Maximum11591414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:38.062373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile4674
Q19848
median18238
Q367978
95-th percentile1238454
Maximum11591414
Range11591164
Interquartile range (IQR)58130

Descriptive statistics

Standard deviation442024.69
Coefficient of variation (CV)2.3040519
Kurtosis121.21044
Mean191846.68
Median Absolute Deviation (MAD)11093
Skewness6.6894481
Sum3.6871013 × 109
Variance1.9538583 × 1011
MonotonicityNot monotonic
2024-03-08T09:02:38.276371image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12587 54
 
0.3%
16308 50
 
0.3%
13352 48
 
0.2%
12886 47
 
0.2%
6884 46
 
0.2%
21321 45
 
0.2%
7446 42
 
0.2%
654358 41
 
0.2%
684236 39
 
0.2%
13351 38
 
0.2%
Other values (2585) 18769
97.7%
ValueCountFrequency (%)
250 1
 
< 0.1%
255 5
< 0.1%
453 1
 
< 0.1%
610 1
 
< 0.1%
612 1
 
< 0.1%
705 1
 
< 0.1%
718 10
0.1%
745 1
 
< 0.1%
763 1
 
< 0.1%
764 10
0.1%
ValueCountFrequency (%)
11591414 5
< 0.1%
4639309 1
 
< 0.1%
3918209 1
 
< 0.1%
3061597 1
 
< 0.1%
3037459 4
< 0.1%
3035252 1
 
< 0.1%
2935414 6
< 0.1%
2918209 1
 
< 0.1%
2712104 4
< 0.1%
2638402 6
< 0.1%

Minimum_of_Luminosity
Real number (ℝ)

HIGH CORRELATION 

Distinct162
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.808419
Minimum0
Maximum196
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:38.482380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile35
Q170
median90
Q3105
95-th percentile120
Maximum196
Range196
Interquartile range (IQR)35

Descriptive statistics

Standard deviation28.800344
Coefficient of variation (CV)0.33959298
Kurtosis-0.0026939772
Mean84.808419
Median Absolute Deviation (MAD)17
Skewness-0.33058067
Sum1629933
Variance829.45981
MonotonicityNot monotonic
2024-03-08T09:02:38.699717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84 540
 
2.8%
77 500
 
2.6%
97 483
 
2.5%
104 464
 
2.4%
91 451
 
2.3%
95 407
 
2.1%
96 407
 
2.1%
99 401
 
2.1%
89 389
 
2.0%
101 380
 
2.0%
Other values (152) 14797
77.0%
ValueCountFrequency (%)
0 2
 
< 0.1%
2 2
 
< 0.1%
4 3
< 0.1%
6 1
 
< 0.1%
7 4
< 0.1%
9 5
< 0.1%
11 6
< 0.1%
14 4
< 0.1%
15 1
 
< 0.1%
16 7
< 0.1%
ValueCountFrequency (%)
196 1
 
< 0.1%
195 14
0.1%
192 9
 
< 0.1%
191 1
 
< 0.1%
190 11
0.1%
179 4
 
< 0.1%
178 24
0.1%
177 4
 
< 0.1%
175 1
 
< 0.1%
173 2
 
< 0.1%

Maximum_of_Luminosity
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.64738
Minimum39
Maximum253
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:38.913377image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile108
Q1124
median127
Q3135
95-th percentile143
Maximum253
Range214
Interquartile range (IQR)11

Descriptive statistics

Standard deviation14.196976
Coefficient of variation (CV)0.11035573
Kurtosis9.7839763
Mean128.64738
Median Absolute Deviation (MAD)6
Skewness1.1711995
Sum2472474
Variance201.55413
MonotonicityNot monotonic
2024-03-08T09:02:39.105383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127 2436
12.7%
124 2146
 
11.2%
126 1936
 
10.1%
141 1550
 
8.1%
132 1237
 
6.4%
125 1019
 
5.3%
133 918
 
4.8%
134 914
 
4.8%
140 805
 
4.2%
135 758
 
3.9%
Other values (88) 5500
28.6%
ValueCountFrequency (%)
39 1
 
< 0.1%
71 12
 
0.1%
77 9
 
< 0.1%
78 7
 
< 0.1%
79 7
 
< 0.1%
82 6
 
< 0.1%
84 77
0.4%
85 1
 
< 0.1%
86 57
0.3%
87 12
 
0.1%
ValueCountFrequency (%)
253 2
 
< 0.1%
252 8
 
< 0.1%
247 2
 
< 0.1%
236 2
 
< 0.1%
222 1
 
< 0.1%
213 4
 
< 0.1%
212 19
0.1%
210 2
 
< 0.1%
207 24
0.1%
206 5
 
< 0.1%

Length_of_Conveyer
Real number (ℝ)

Distinct99
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1459.3507
Minimum1227
Maximum1794
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:39.295380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1227
5-th percentile1353
Q11358
median1364
Q31652
95-th percentile1692
Maximum1794
Range567
Interquartile range (IQR)294

Descriptive statistics

Standard deviation145.56869
Coefficient of variation (CV)0.099748938
Kurtosis-1.2141739
Mean1459.3507
Median Absolute Deviation (MAD)9
Skewness0.85524698
Sum28047262
Variance21190.243
MonotonicityNot monotonic
2024-03-08T09:02:39.495228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1358 2844
14.8%
1356 1950
 
10.1%
1360 1720
 
8.9%
1362 1251
 
6.5%
1364 1183
 
6.2%
1692 1141
 
5.9%
1687 973
 
5.1%
1690 784
 
4.1%
1353 761
 
4.0%
1354 636
 
3.3%
Other values (89) 5976
31.1%
ValueCountFrequency (%)
1227 3
 
< 0.1%
1280 5
 
< 0.1%
1300 1
 
< 0.1%
1306 23
0.1%
1308 9
 
< 0.1%
1318 1
 
< 0.1%
1320 6
 
< 0.1%
1322 23
0.1%
1324 1
 
< 0.1%
1327 1
 
< 0.1%
ValueCountFrequency (%)
1794 1
 
< 0.1%
1715 5
 
< 0.1%
1712 4
 
< 0.1%
1710 39
 
0.2%
1708 8
 
< 0.1%
1707 3
 
< 0.1%
1700 10
 
0.1%
1698 179
0.9%
1696 226
1.2%
1694 324
1.7%

TypeOfSteel_A300
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
11480 
1
7739 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19219
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11480
59.7%
1 7739
40.3%

Length

2024-03-08T09:02:39.673307image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T09:02:39.816382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 11480
59.7%
1 7739
40.3%

Most occurring characters

ValueCountFrequency (%)
0 11480
59.7%
1 7739
40.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19219
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11480
59.7%
1 7739
40.3%

Most occurring scripts

ValueCountFrequency (%)
Common 19219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11480
59.7%
1 7739
40.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11480
59.7%
1 7739
40.3%

TypeOfSteel_A400
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1
11461 
0
7758 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19219
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 11461
59.6%
0 7758
40.4%

Length

2024-03-08T09:02:39.961501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T09:02:40.097964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 11461
59.6%
0 7758
40.4%

Most occurring characters

ValueCountFrequency (%)
1 11461
59.6%
0 7758
40.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19219
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11461
59.6%
0 7758
40.4%

Most occurring scripts

ValueCountFrequency (%)
Common 19219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11461
59.6%
0 7758
40.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11461
59.6%
0 7758
40.4%

Steel_Plate_Thickness
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.213122
Minimum40
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:40.243415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile40
Q140
median69
Q380
95-th percentile200
Maximum300
Range260
Interquartile range (IQR)40

Descriptive statistics

Standard deviation53.93196
Coefficient of variation (CV)0.70764664
Kurtosis5.7593797
Mean76.213122
Median Absolute Deviation (MAD)29
Skewness2.3574486
Sum1464740
Variance2908.6563
MonotonicityNot monotonic
2024-03-08T09:02:40.415984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
40 7625
39.7%
70 3703
19.3%
80 1661
 
8.6%
100 1433
 
7.5%
50 1011
 
5.3%
60 856
 
4.5%
200 748
 
3.9%
300 434
 
2.3%
175 396
 
2.1%
69 283
 
1.5%
Other values (17) 1069
 
5.6%
ValueCountFrequency (%)
40 7625
39.7%
50 1011
 
5.3%
60 856
 
4.5%
69 283
 
1.5%
70 3703
19.3%
80 1661
 
8.6%
81 1
 
< 0.1%
85 21
 
0.1%
86 1
 
< 0.1%
90 211
 
1.1%
ValueCountFrequency (%)
300 434
2.3%
290 11
 
0.1%
250 8
 
< 0.1%
220 105
 
0.5%
211 18
 
0.1%
200 748
3.9%
185 116
 
0.6%
180 7
 
< 0.1%
175 396
2.1%
159 1
 
< 0.1%

Edges_Index
Real number (ℝ)

ZEROS 

Distinct1849
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.35293936
Minimum0
Maximum0.9952
Zeros447
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:40.605397image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0071
Q10.0586
median0.2385
Q30.6561
95-th percentile0.9135
Maximum0.9952
Range0.9952
Interquartile range (IQR)0.5975

Descriptive statistics

Standard deviation0.31897603
Coefficient of variation (CV)0.90377008
Kurtosis-1.2068928
Mean0.35293936
Median Absolute Deviation (MAD)0.196
Skewness0.54336054
Sum6783.1416
Variance0.10174571
MonotonicityNot monotonic
2024-03-08T09:02:40.811160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0604 761
 
4.0%
0.0574 529
 
2.8%
0 447
 
2.3%
0.0557 334
 
1.7%
0.0605 294
 
1.5%
0.0575 257
 
1.3%
0.0585 256
 
1.3%
0.0556 188
 
1.0%
0.0586 165
 
0.9%
0.0325 157
 
0.8%
Other values (1839) 15831
82.4%
ValueCountFrequency (%)
0 447
2.3%
0.0012 7
 
< 0.1%
0.0014 7
 
< 0.1%
0.0015 35
 
0.2%
0.0023 7
 
< 0.1%
0.0024 124
 
0.6%
0.003 6
 
< 0.1%
0.0035 3
 
< 0.1%
0.0036 17
 
0.1%
0.0041 1
 
< 0.1%
ValueCountFrequency (%)
0.9952 10
0.1%
0.9929 1
 
< 0.1%
0.9923 8
< 0.1%
0.9905 10
0.1%
0.9897 4
 
< 0.1%
0.9846 19
0.1%
0.9836 3
 
< 0.1%
0.9835 4
 
< 0.1%
0.9834 8
< 0.1%
0.9816 5
 
< 0.1%

Empty_Index
Real number (ℝ)

HIGH CORRELATION 

Distinct1748
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.40930948
Minimum0
Maximum0.9275
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:41.007707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2153
Q10.3175
median0.4135
Q30.4946
95-th percentile0.6234
Maximum0.9275
Range0.9275
Interquartile range (IQR)0.1771

Descriptive statistics

Standard deviation0.12414349
Coefficient of variation (CV)0.30329982
Kurtosis-0.17418332
Mean0.40930948
Median Absolute Deviation (MAD)0.0892
Skewness0.19922437
Sum7866.5189
Variance0.015411606
MonotonicityNot monotonic
2024-03-08T09:02:41.206409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3333 289
 
1.5%
0.25 229
 
1.2%
0.2778 177
 
0.9%
0.2 164
 
0.9%
0.4167 150
 
0.8%
0.4286 139
 
0.7%
0.3636 139
 
0.7%
0.3 137
 
0.7%
0.4 126
 
0.7%
0.5 122
 
0.6%
Other values (1738) 17547
91.3%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.0118 1
 
< 0.1%
0.0278 4
 
< 0.1%
0.0322 1
 
< 0.1%
0.0368 3
 
< 0.1%
0.0682 3
 
< 0.1%
0.0707 1
 
< 0.1%
0.0714 11
0.1%
0.0781 9
< 0.1%
0.0818 2
 
< 0.1%
ValueCountFrequency (%)
0.9275 1
 
< 0.1%
0.894 4
< 0.1%
0.8893 1
 
< 0.1%
0.8888 3
< 0.1%
0.8887 1
 
< 0.1%
0.8856 1
 
< 0.1%
0.8817 1
 
< 0.1%
0.8815 1
 
< 0.1%
0.8767 2
< 0.1%
0.8487 3
< 0.1%

Square_Index
Real number (ℝ)

Distinct1118
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57452039
Minimum0.0083
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:41.588094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0083
5-th percentile0.15863
Q10.37575
median0.5454
Q30.8182
95-th percentile0.96233
Maximum1
Range0.9917
Interquartile range (IQR)0.44245

Descriptive statistics

Standard deviation0.25943588
Coefficient of variation (CV)0.45156949
Kurtosis-1.1548399
Mean0.57452039
Median Absolute Deviation (MAD)0.2046
Skewness0.010508861
Sum11041.707
Variance0.067306975
MonotonicityNot monotonic
2024-03-08T09:02:41.794927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 723
 
3.8%
0.8 545
 
2.8%
0.5 506
 
2.6%
0.6667 398
 
2.1%
0.4 389
 
2.0%
0.3333 366
 
1.9%
0.8889 349
 
1.8%
0.8571 346
 
1.8%
0.75 306
 
1.6%
0.9167 291
 
1.5%
Other values (1108) 15000
78.0%
ValueCountFrequency (%)
0.0083 1
 
< 0.1%
0.0261 3
< 0.1%
0.0294 7
< 0.1%
0.0296 1
 
< 0.1%
0.0368 1
 
< 0.1%
0.0374 1
 
< 0.1%
0.039 1
 
< 0.1%
0.0393 6
< 0.1%
0.03939 2
 
< 0.1%
0.0396 2
 
< 0.1%
ValueCountFrequency (%)
1 723
3.8%
0.9976 1
 
< 0.1%
0.9955 3
 
< 0.1%
0.9945 2
 
< 0.1%
0.9944 1
 
< 0.1%
0.9943 1
 
< 0.1%
0.9942 29
 
0.2%
0.993 2
 
< 0.1%
0.9915 1
 
< 0.1%
0.9912 6
 
< 0.1%

Outside_X_Index
Real number (ℝ)

HIGH CORRELATION 

Distinct525
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.030609355
Minimum0.0015
Maximum0.6651
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:41.988955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0015
5-th percentile0.0044
Q10.0066
median0.0095
Q30.0191
95-th percentile0.1289
Maximum0.6651
Range0.6636
Interquartile range (IQR)0.0125

Descriptive statistics

Standard deviation0.047301941
Coefficient of variation (CV)1.5453426
Kurtosis20.010556
Mean0.030609355
Median Absolute Deviation (MAD)0.0041
Skewness3.1192074
Sum588.2812
Variance0.0022374737
MonotonicityNot monotonic
2024-03-08T09:02:42.191354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0059 921
 
4.8%
0.0066 824
 
4.3%
0.0088 687
 
3.6%
0.0081 665
 
3.5%
0.0047 514
 
2.7%
0.0053 499
 
2.6%
0.0103 491
 
2.6%
0.0044 476
 
2.5%
0.0071 472
 
2.5%
0.0074 459
 
2.4%
Other values (515) 13211
68.7%
ValueCountFrequency (%)
0.0015 3
 
< 0.1%
0.0022 10
 
0.1%
0.0024 2
 
< 0.1%
0.0029 18
 
0.1%
0.003 23
 
0.1%
0.0034 1
 
< 0.1%
0.0035 22
 
0.1%
0.0036 65
 
0.3%
0.0037 279
1.5%
0.0038 1
 
< 0.1%
ValueCountFrequency (%)
0.6651 1
 
< 0.1%
0.6228 1
 
< 0.1%
0.6226 2
 
< 0.1%
0.6209 2
 
< 0.1%
0.5906 1
 
< 0.1%
0.5692 4
< 0.1%
0.4964 6
< 0.1%
0.4957 5
< 0.1%
0.4698 2
 
< 0.1%
0.3878 3
< 0.1%

Edges_X_Index
Real number (ℝ)

HIGH CORRELATION 

Distinct1102
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.61474948
Minimum0.0144
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:42.402456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0144
5-th percentile0.2358
Q10.4516
median0.6364
Q30.7857
95-th percentile1
Maximum1
Range0.9856
Interquartile range (IQR)0.3341

Descriptive statistics

Standard deviation0.22239127
Coefficient of variation (CV)0.36175919
Kurtosis-0.77946537
Mean0.61474948
Median Absolute Deviation (MAD)0.1636
Skewness-0.24619622
Sum11814.87
Variance0.049457878
MonotonicityNot monotonic
2024-03-08T09:02:42.610410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 968
 
5.0%
0.8 723
 
3.8%
0.5 581
 
3.0%
0.6667 497
 
2.6%
0.75 453
 
2.4%
0.8571 264
 
1.4%
0.8333 253
 
1.3%
0.7143 228
 
1.2%
0.5833 216
 
1.1%
0.8889 214
 
1.1%
Other values (1092) 14822
77.1%
ValueCountFrequency (%)
0.0144 1
 
< 0.1%
0.0704 1
 
< 0.1%
0.0714 2
< 0.1%
0.0717 4
< 0.1%
0.0724 3
< 0.1%
0.0734 1
 
< 0.1%
0.0754 1
 
< 0.1%
0.0782 3
< 0.1%
0.0794 2
< 0.1%
0.0795 1
 
< 0.1%
ValueCountFrequency (%)
1 968
5.0%
0.9879 1
 
< 0.1%
0.9828 4
 
< 0.1%
0.9796 1
 
< 0.1%
0.9776 1
 
< 0.1%
0.975 8
 
< 0.1%
0.9688 21
 
0.1%
0.9682 4
 
< 0.1%
0.9671 7
 
< 0.1%
0.9655 1
 
< 0.1%

Edges_Y_Index
Real number (ℝ)

HIGH CORRELATION 

Distinct900
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8316521
Minimum0.105
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:42.811918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.105
5-th percentile0.4118
Q10.6552
median0.9643
Q31
95-th percentile1
Maximum1
Range0.895
Interquartile range (IQR)0.3448

Descriptive statistics

Standard deviation0.22096603
Coefficient of variation (CV)0.26569527
Kurtosis-0.56140074
Mean0.8316521
Median Absolute Deviation (MAD)0.0357
Skewness-0.98445142
Sum15983.522
Variance0.048825985
MonotonicityNot monotonic
2024-03-08T09:02:43.003409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 8659
45.1%
0.6667 201
 
1.0%
0.9231 198
 
1.0%
0.5 192
 
1.0%
0.8333 167
 
0.9%
0.8 151
 
0.8%
0.9286 147
 
0.8%
0.8182 135
 
0.7%
0.75 133
 
0.7%
0.8571 133
 
0.7%
Other values (890) 9103
47.4%
ValueCountFrequency (%)
0.105 2
 
< 0.1%
0.1123 2
 
< 0.1%
0.1132 1
 
< 0.1%
0.1229 1
 
< 0.1%
0.1272 1
 
< 0.1%
0.1312 6
< 0.1%
0.1321 7
< 0.1%
0.1329 1
 
< 0.1%
0.1378 1
 
< 0.1%
0.1379 3
< 0.1%
ValueCountFrequency (%)
1 8659
45.1%
0.9995 1
 
< 0.1%
0.9994 3
 
< 0.1%
0.9992 1
 
< 0.1%
0.9977 2
 
< 0.1%
0.9973 5
 
< 0.1%
0.9972 1
 
< 0.1%
0.9955 1
 
< 0.1%
0.9953 1
 
< 0.1%
0.9931 16
 
0.1%

Outside_Global_Index
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
1.0
11022 
0.0
7489 
0.5
 
706
-0.0
 
1
0.7
 
1

Length

Max length4
Median length3
Mean length3.000052
Min length3

Characters and Unicode

Total characters57658
Distinct characters6
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 11022
57.3%
0.0 7489
39.0%
0.5 706
 
3.7%
-0.0 1
 
< 0.1%
0.7 1
 
< 0.1%

Length

2024-03-08T09:02:43.176218image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T09:02:43.326036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 11022
57.3%
0.0 7490
39.0%
0.5 706
 
3.7%
0.7 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 26709
46.3%
. 19219
33.3%
1 11022
19.1%
5 706
 
1.2%
- 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38438
66.7%
Other Punctuation 19219
33.3%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26709
69.5%
1 11022
28.7%
5 706
 
1.8%
7 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 19219
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57658
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26709
46.3%
. 19219
33.3%
1 11022
19.1%
5 706
 
1.2%
- 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57658
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26709
46.3%
. 19219
33.3%
1 11022
19.1%
5 706
 
1.2%
- 1
 
< 0.1%
7 1
 
< 0.1%

LogOfAreas
Real number (ℝ)

HIGH CORRELATION 

Distinct1072
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4734746
Minimum0.7782
Maximum4.5543
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:43.507460image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.7782
5-th percentile1.7076
Q11.9494
median2.2279
Q32.8149
95-th percentile3.9812
Maximum4.5543
Range3.7761
Interquartile range (IQR)0.8655

Descriptive statistics

Standard deviation0.76057514
Coefficient of variation (CV)0.3074926
Kurtosis-0.25330946
Mean2.4734746
Median Absolute Deviation (MAD)0.3587
Skewness0.85908048
Sum47537.708
Variance0.57847454
MonotonicityNot monotonic
2024-03-08T09:02:43.721491image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.716 262
 
1.4%
2.0043 227
 
1.2%
2.0569 222
 
1.2%
1.7404 203
 
1.1%
1.8692 180
 
0.9%
2.0334 179
 
0.9%
2.0414 168
 
0.9%
2.2304 166
 
0.9%
2.1461 162
 
0.8%
2.2648 161
 
0.8%
Other values (1062) 17289
90.0%
ValueCountFrequency (%)
0.7782 22
 
0.1%
0.8451 2
 
< 0.1%
0.9031 3
 
< 0.1%
0.9138 1
 
< 0.1%
0.9542 4
 
< 0.1%
1 3
 
< 0.1%
1.0414 7
 
< 0.1%
1.0792 97
0.5%
1.1139 1
 
< 0.1%
1.1461 1
 
< 0.1%
ValueCountFrequency (%)
4.5543 1
 
< 0.1%
4.4885 1
 
< 0.1%
4.4061 2
< 0.1%
4.4035 2
< 0.1%
4.3962 1
 
< 0.1%
4.3961 1
 
< 0.1%
4.3868 3
< 0.1%
4.3632 1
 
< 0.1%
4.3631 1
 
< 0.1%
4.3581 1
 
< 0.1%

Log_X_Index
Real number (ℝ)

HIGH CORRELATION 

Distinct206
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3126668
Minimum0.301
Maximum2.9973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:43.932421image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.301
5-th percentile0.8451
Q11
median1.1461
Q31.4314
95-th percentile2.243
Maximum2.9973
Range2.6963
Interquartile range (IQR)0.4314

Descriptive statistics

Standard deviation0.46784765
Coefficient of variation (CV)0.35641007
Kurtosis-0.064481267
Mean1.3126668
Median Absolute Deviation (MAD)0.1919
Skewness1.0983306
Sum25228.143
Variance0.21888142
MonotonicityNot monotonic
2024-03-08T09:02:44.138287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0792 1547
 
8.0%
0.9542 1503
 
7.8%
0.9031 1307
 
6.8%
1.0414 1270
 
6.6%
1 1150
 
6.0%
1.1139 1019
 
5.3%
1.1461 790
 
4.1%
0.8451 739
 
3.8%
1.2305 675
 
3.5%
1.1761 590
 
3.1%
Other values (196) 8629
44.9%
ValueCountFrequency (%)
0.301 2
 
< 0.1%
0.4771 9
 
< 0.1%
0.6021 40
 
0.2%
0.699 278
 
1.4%
0.7634 1
 
< 0.1%
0.7782 576
3.0%
0.7903 1
 
< 0.1%
0.8451 739
3.8%
0.8772 1
 
< 0.1%
0.9031 1307
6.8%
ValueCountFrequency (%)
2.9973 1
 
< 0.1%
2.9385 4
< 0.1%
2.9335 2
 
< 0.1%
2.8882 1
 
< 0.1%
2.842 8
< 0.1%
2.8414 3
 
< 0.1%
2.8048 1
 
< 0.1%
2.7543 1
 
< 0.1%
2.7324 1
 
< 0.1%
2.7235 4
< 0.1%

Log_Y_Index
Real number (ℝ)

HIGH CORRELATION 

Distinct241
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3897366
Minimum0
Maximum4.0333
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:44.340312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.7782
Q11.0792
median1.3222
Q31.7076
95-th percentile2.2014
Maximum4.0333
Range4.0333
Interquartile range (IQR)0.6284

Descriptive statistics

Standard deviation0.4055493
Coefficient of variation (CV)0.29181738
Kurtosis-0.17335392
Mean1.3897366
Median Absolute Deviation (MAD)0.2689
Skewness0.38716021
Sum26709.348
Variance0.16447023
MonotonicityNot monotonic
2024-03-08T09:02:44.562405image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0792 1073
 
5.6%
1.0414 917
 
4.8%
1.1461 729
 
3.8%
1 687
 
3.6%
1.1139 662
 
3.4%
1.1761 617
 
3.2%
1.301 590
 
3.1%
1.2553 577
 
3.0%
0.9031 515
 
2.7%
1.2305 489
 
2.5%
Other values (231) 12363
64.3%
ValueCountFrequency (%)
0 3
 
< 0.1%
0.301 17
 
0.1%
0.4771 102
 
0.5%
0.6021 405
2.1%
0.6432 1
 
< 0.1%
0.699 280
1.5%
0.7782 258
1.3%
0.8451 262
1.4%
0.9031 515
2.7%
0.9542 439
2.3%
ValueCountFrequency (%)
4.0333 1
 
< 0.1%
3.3508 1
 
< 0.1%
3.0394 1
 
< 0.1%
2.776 2
 
< 0.1%
2.6464 1
 
< 0.1%
2.6405 1
 
< 0.1%
2.6294 4
< 0.1%
2.6181 7
< 0.1%
2.6149 1
 
< 0.1%
2.6021 1
 
< 0.1%

Orientation_Index
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1598
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10274234
Minimum-0.9884
Maximum0.9917
Zeros735
Zeros (%)3.8%
Negative7493
Negative (%)39.0%
Memory size150.3 KiB
2024-03-08T09:02:44.775971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.9884
5-th percentile-0.6333
Q1-0.2727
median0.1111
Q30.5294
95-th percentile0.8082
Maximum0.9917
Range1.9801
Interquartile range (IQR)0.8021

Descriptive statistics

Standard deviation0.48768051
Coefficient of variation (CV)4.746636
Kurtosis-1.0936499
Mean0.10274234
Median Absolute Deviation (MAD)0.4052
Skewness-0.18917339
Sum1974.6051
Variance0.23783228
MonotonicityNot monotonic
2024-03-08T09:02:44.994425image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 735
 
3.8%
0.5 304
 
1.6%
0.6667 284
 
1.5%
-0.2 284
 
1.5%
0.2 261
 
1.4%
0.3333 243
 
1.3%
0.1818 207
 
1.1%
-0.5 202
 
1.1%
-0.6 200
 
1.0%
0.6 189
 
1.0%
Other values (1588) 16310
84.9%
ValueCountFrequency (%)
-0.9884 1
 
< 0.1%
-0.9809 1
 
< 0.1%
-0.9706 5
< 0.1%
-0.9604 2
 
< 0.1%
-0.9592 2
 
< 0.1%
-0.9565 1
 
< 0.1%
-0.9559 3
< 0.1%
-0.9552 3
< 0.1%
-0.9546 4
< 0.1%
-0.9514 3
< 0.1%
ValueCountFrequency (%)
0.9917 1
 
< 0.1%
0.9739 3
< 0.1%
0.9706 2
 
< 0.1%
0.9661 2
 
< 0.1%
0.9632 1
 
< 0.1%
0.961 1
 
< 0.1%
0.9607 6
< 0.1%
0.9604 1
 
< 0.1%
0.9592 3
< 0.1%
0.9578 2
 
< 0.1%

Luminosity_Index
Real number (ℝ)

HIGH CORRELATION 

Distinct2046
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.13838179
Minimum-0.885
Maximum0.6421
Zeros1
Zeros (%)< 0.1%
Negative17707
Negative (%)92.1%
Memory size150.3 KiB
2024-03-08T09:02:45.193361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-0.885
5-th percentile-0.3061
Q1-0.1925
median-0.1426
Q3-0.084
95-th percentile0.0101
Maximum0.6421
Range1.5271
Interquartile range (IQR)0.1085

Descriptive statistics

Standard deviation0.120344
Coefficient of variation (CV)-0.86965205
Kurtosis6.9017166
Mean-0.13838179
Median Absolute Deviation (MAD)0.0534
Skewness0.77771247
Sum-2659.5595
Variance0.014482679
MonotonicityNot monotonic
2024-03-08T09:02:45.398786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.1851 117
 
0.6%
-0.1865 93
 
0.5%
-0.1861 80
 
0.4%
-0.1078 79
 
0.4%
-0.1903 79
 
0.4%
-0.0971 77
 
0.4%
-0.2395 76
 
0.4%
-0.0481 73
 
0.4%
-0.1855 68
 
0.4%
-0.0019 68
 
0.4%
Other values (2036) 18409
95.8%
ValueCountFrequency (%)
-0.885 3
 
< 0.1%
-0.6096 2
 
< 0.1%
-0.6017 4
< 0.1%
-0.5986 1
 
< 0.1%
-0.5971 9
< 0.1%
-0.594 3
 
< 0.1%
-0.5902 1
 
< 0.1%
-0.585 3
 
< 0.1%
-0.5816 3
 
< 0.1%
-0.5811 1
 
< 0.1%
ValueCountFrequency (%)
0.6421 1
 
< 0.1%
0.5917 3
 
< 0.1%
0.5916 10
0.1%
0.5909 4
 
< 0.1%
0.5799 10
0.1%
0.5613 3
 
< 0.1%
0.5591 4
 
< 0.1%
0.5552 6
< 0.1%
0.5518 5
< 0.1%
0.5412 1
 
< 0.1%

SigmoidOfAreas
Real number (ℝ)

HIGH CORRELATION 

Distinct467
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.57190219
Minimum0.119
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size150.3 KiB
2024-03-08T09:02:45.611437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.119
5-th percentile0.1753
Q10.2532
median0.4729
Q30.9994
95-th percentile1
Maximum1
Range0.881
Interquartile range (IQR)0.7462

Descriptive statistics

Standard deviation0.33221863
Coefficient of variation (CV)0.58090112
Kurtosis-1.6376523
Mean0.57190219
Median Absolute Deviation (MAD)0.2711
Skewness0.22253574
Sum10991.388
Variance0.11036922
MonotonicityNot monotonic
2024-03-08T09:02:45.807426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4075
 
21.2%
0.1773 366
 
1.9%
0.9998 352
 
1.8%
0.2901 348
 
1.8%
0.2288 291
 
1.5%
0.2432 278
 
1.4%
0.1852 266
 
1.4%
0.2173 230
 
1.2%
0.3068 226
 
1.2%
0.2583 223
 
1.2%
Other values (457) 12564
65.4%
ValueCountFrequency (%)
0.119 2
 
< 0.1%
0.1232 1
 
< 0.1%
0.124 9
 
< 0.1%
0.1262 22
 
0.1%
0.1284 6
 
< 0.1%
0.1292 23
 
0.1%
0.1307 11
 
0.1%
0.1322 138
0.7%
0.133 7
 
< 0.1%
0.1353 69
0.4%
ValueCountFrequency (%)
1 4075
21.2%
0.9999 185
 
1.0%
0.9998 352
 
1.8%
0.9997 96
 
0.5%
0.9996 52
 
0.3%
0.9995 19
 
0.1%
0.9994 39
 
0.2%
0.9993 48
 
0.2%
0.9992 29
 
0.2%
0.9991 20
 
0.1%

Pastry
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
17753 
1
 
1466

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19219
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 17753
92.4%
1 1466
 
7.6%

Length

2024-03-08T09:02:46.154207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T09:02:46.284209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17753
92.4%
1 1466
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 17753
92.4%
1 1466
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19219
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17753
92.4%
1 1466
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Common 19219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17753
92.4%
1 1466
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17753
92.4%
1 1466
 
7.6%

Z_Scratch
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
18069 
1
 
1150

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19219
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18069
94.0%
1 1150
 
6.0%

Length

2024-03-08T09:02:46.422385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T09:02:46.551427image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18069
94.0%
1 1150
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 18069
94.0%
1 1150
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19219
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18069
94.0%
1 1150
 
6.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18069
94.0%
1 1150
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18069
94.0%
1 1150
 
6.0%

K_Scatch
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
15787 
1
3432 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19219
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 15787
82.1%
1 3432
 
17.9%

Length

2024-03-08T09:02:46.691755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T09:02:46.826473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 15787
82.1%
1 3432
 
17.9%

Most occurring characters

ValueCountFrequency (%)
0 15787
82.1%
1 3432
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19219
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15787
82.1%
1 3432
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common 19219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15787
82.1%
1 3432
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15787
82.1%
1 3432
 
17.9%

Stains
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
18651 
1
 
568

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19219
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18651
97.0%
1 568
 
3.0%

Length

2024-03-08T09:02:46.970434image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T09:02:47.101435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18651
97.0%
1 568
 
3.0%

Most occurring characters

ValueCountFrequency (%)
0 18651
97.0%
1 568
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19219
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18651
97.0%
1 568
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 19219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18651
97.0%
1 568
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18651
97.0%
1 568
 
3.0%

Dirtiness
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
18734 
1
 
485

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19219
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 18734
97.5%
1 485
 
2.5%

Length

2024-03-08T09:02:47.239219image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T09:02:47.375836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 18734
97.5%
1 485
 
2.5%

Most occurring characters

ValueCountFrequency (%)
0 18734
97.5%
1 485
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19219
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18734
97.5%
1 485
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 19219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18734
97.5%
1 485
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18734
97.5%
1 485
 
2.5%

Bumps
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
14456 
1
4763 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19219
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14456
75.2%
1 4763
 
24.8%

Length

2024-03-08T09:02:47.514884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T09:02:47.648436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14456
75.2%
1 4763
 
24.8%

Most occurring characters

ValueCountFrequency (%)
0 14456
75.2%
1 4763
 
24.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19219
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14456
75.2%
1 4763
 
24.8%

Most occurring scripts

ValueCountFrequency (%)
Common 19219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14456
75.2%
1 4763
 
24.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14456
75.2%
1 4763
 
24.8%

Other_Faults
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.1 MiB
0
12661 
1
6558 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters19219
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 12661
65.9%
1 6558
34.1%

Length

2024-03-08T09:02:47.791442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T09:02:47.926440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 12661
65.9%
1 6558
34.1%

Most occurring characters

ValueCountFrequency (%)
0 12661
65.9%
1 6558
34.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19219
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12661
65.9%
1 6558
34.1%

Most occurring scripts

ValueCountFrequency (%)
Common 19219
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12661
65.9%
1 6558
34.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19219
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12661
65.9%
1 6558
34.1%

Interactions

2024-03-08T09:02:29.457283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:00:58.519062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:02.483152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:06.221810image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:10.116550image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:13.789957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:17.697330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:21.586919image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:25.549461image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:29.053357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:32.766858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:36.849326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:40.424178image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:44.319581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:47.981287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:51.757036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:55.320981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:59.117114image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:02.799406image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:06.398171image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:10.128387image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:13.915928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:17.725117image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:21.568940image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:25.662514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:29.614902image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:00:58.688923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:02.642358image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:06.388480image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:10.274554image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:13.950817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:17.860854image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:21.746450image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:25.696466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:29.210934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:32.930551image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:37.003887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:40.585601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:44.474232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:48.135444image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:51.905622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:55.475519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:59.274533image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:02.952836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:06.551402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:10.288756image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:14.068030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:17.889870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:21.730270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:25.821172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:29.762499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:00:58.846936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:02.797153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:06.537517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:10.427361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:14.104819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:18.021852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:21.899880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:25.838962image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:29.368771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:33.090097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:37.148890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:40.736528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:44.623229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:48.282468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:52.050761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:55.627872image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:59.425309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:03.100082image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:06.696665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:10.441727image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:14.218728image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:18.050330image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:21.890407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:25.975953image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:29.907895image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:00:59.003456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:02.950760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:06.687239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:10.579454image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:14.257687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:18.181849image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:22.052486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:25.983341image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:29.521091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:33.438949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:37.297251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:40.887232image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:44.778233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:48.431479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:52.195072image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:55.774231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:59.577828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:03.246144image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:07.044501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:10.593193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:14.365746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:18.205270image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:22.050273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:26.130293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:30.050325image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:00:59.157274image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:03.105311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:06.835488image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:10.725701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:14.410643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:18.342760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:22.203878image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:26.126544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:29.669924image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:33.596697image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:37.439656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:41.036775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:44.926380image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:48.574005image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:52.346719image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:55.926014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:59.723828image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:03.388818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:07.183499image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:10.744974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:14.512748image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:18.363226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:22.207058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:26.281885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:30.189242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:00:59.313407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:03.254761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:06.981487image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:10.869441image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:14.555832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:18.500759image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:22.354787image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:26.267640image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:29.817443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:33.756414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-03-08T09:01:39.827443image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:43.695716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:47.378267image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:51.158682image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:54.735088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:58.349129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:02.189142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:05.801000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:09.548187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:13.288168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:17.121237image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:20.943350image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:25.020300image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:28.832348image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:32.746352image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:02.017798image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:05.771679image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:09.658115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:13.341826image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:17.231839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:21.117881image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:25.083344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:28.627423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:32.316148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:36.376420image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:39.981501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:43.857780image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:47.538045image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:51.312581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:54.882842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:58.501301image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:02.347137image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:05.954105image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:09.697987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:13.449729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:17.280180image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:21.103817image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:25.190092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:28.995776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:32.903899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:02.182091image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:05.932781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:09.820786image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:13.502567image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:17.399966image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:21.283891image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:25.248294image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:28.779970image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:32.477462image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:36.543208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:40.141731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:44.021019image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:47.693601image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:51.475042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:55.038701image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:58.836115image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:02.512568image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:06.114834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:09.853731image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:13.614639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:17.436583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:21.268260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:25.358009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:29.161310image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:33.054911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:02.342366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:06.086808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:09.978094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:13.653340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:17.555534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:21.444920image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:25.405392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:28.925098image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:32.633324image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:36.704312image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:40.291662image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:44.180175image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:47.847255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:51.624074image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:55.188710image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:01:58.987936image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:02.665054image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:06.265064image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:09.999209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:13.771927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:17.587595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:21.426273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:25.519776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T09:02:29.318335image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T09:02:48.099437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
BumpsDirtinessEdges_IndexEdges_X_IndexEdges_Y_IndexEmpty_IndexK_ScatchLength_of_ConveyerLogOfAreasLog_X_IndexLog_Y_IndexLuminosity_IndexMaximum_of_LuminosityMinimum_of_LuminosityOrientation_IndexOther_FaultsOutside_Global_IndexOutside_X_IndexPastryPixels_AreasSigmoidOfAreasSquare_IndexStainsSteel_Plate_ThicknessSum_of_LuminosityTypeOfSteel_A300TypeOfSteel_A400X_MaximumX_MinimumX_PerimeterY_MaximumY_MinimumY_PerimeterZ_Scratchid
Bumps1.0000.0920.2040.1590.197-0.1470.2670.159-0.218-0.135-0.243-0.049-0.0660.0890.0210.4130.088-0.1490.165-0.218-0.2200.1750.1000.125-0.2180.2460.2450.1770.192-0.2150.0340.033-0.2500.1440.000
Dirtiness0.0921.0000.052-0.0960.077-0.0130.074-0.0370.012-0.0810.0570.0630.0400.0870.1540.1150.105-0.0700.0450.0120.010-0.1300.0260.0340.0140.0790.0800.0390.043-0.007-0.006-0.0030.0370.039-0.003
Edges_Index0.2040.0521.0000.2330.264-0.1610.4940.078-0.380-0.285-0.3820.2240.1590.3840.0370.1310.085-0.2760.045-0.380-0.3680.1260.1640.180-0.3520.1900.1880.1620.189-0.350-0.019-0.017-0.3910.121-0.007
Edges_X_Index0.159-0.0960.2331.0000.183-0.3320.3620.037-0.540-0.200-0.6320.032-0.0540.195-0.4160.0970.237-0.2170.136-0.539-0.5300.3080.2820.052-0.5240.2320.2300.0820.094-0.5620.0380.039-0.6090.076-0.010
Edges_Y_Index0.1970.0770.2640.1831.000-0.5800.7510.204-0.593-0.803-0.440-0.096-0.1850.2690.5370.2170.339-0.8050.217-0.592-0.6330.0820.0840.356-0.6070.3190.3160.3130.367-0.745-0.045-0.045-0.5730.091-0.001
Empty_Index-0.147-0.013-0.161-0.332-0.5801.0000.309-0.1770.4320.5350.3710.1530.180-0.032-0.1790.0710.1610.5480.1400.4320.492-0.1170.067-0.1580.4420.1930.192-0.149-0.1700.576-0.019-0.0210.4510.088-0.001
K_Scatch0.2670.0740.4940.3620.7510.3091.000-0.1740.5820.5900.516-0.0530.080-0.500-0.4050.3300.3340.5940.1330.5820.563-0.0490.081-0.4930.5800.3750.372-0.399-0.4740.5810.1060.1050.5620.117-0.000
Length_of_Conveyer0.159-0.0370.0780.0370.204-0.177-0.1741.000-0.151-0.169-0.127-0.094-0.029-0.0640.1570.1580.112-0.2550.168-0.151-0.1760.1530.1210.269-0.1650.4700.4670.2340.239-0.185-0.010-0.011-0.1590.1820.001
LogOfAreas-0.2180.012-0.380-0.540-0.5930.4320.582-0.1511.0000.7470.905-0.1670.010-0.517-0.0360.2480.2670.7490.1420.9980.962-0.3200.684-0.3170.9680.4320.428-0.306-0.3660.8780.0310.0300.9360.126-0.004
Log_X_Index-0.135-0.081-0.285-0.200-0.8030.5350.590-0.1690.7471.0000.585-0.0600.076-0.405-0.4670.2470.2830.9830.1920.7470.788-0.0970.395-0.3530.7480.3740.370-0.305-0.3660.8630.0560.0560.6830.134-0.003
Log_Y_Index-0.2430.057-0.382-0.632-0.4400.3710.516-0.1270.9050.5851.000-0.177-0.009-0.4970.1910.1880.2630.5910.1320.9050.898-0.4060.567-0.2570.8780.3990.396-0.271-0.3260.7930.0200.0190.9620.076-0.006
Luminosity_Index-0.0490.0630.2240.032-0.0960.153-0.053-0.094-0.167-0.060-0.1771.0000.8320.726-0.0920.0920.089-0.0340.114-0.167-0.1470.1010.184-0.136-0.0910.2290.229-0.064-0.048-0.069-0.042-0.045-0.1390.0480.006
Maximum_of_Luminosity-0.0660.0400.159-0.054-0.1850.1800.080-0.0290.0100.076-0.0090.8321.0000.462-0.1000.0540.0810.0910.0690.0090.0180.0930.108-0.1520.0760.2300.229-0.120-0.1200.084-0.049-0.0520.0240.0740.001
Minimum_of_Luminosity0.0890.0870.3840.1950.269-0.032-0.500-0.064-0.517-0.405-0.4970.7260.4621.0000.0980.2330.209-0.3790.136-0.516-0.4730.0480.2230.144-0.4530.3850.3830.1900.252-0.430-0.095-0.094-0.4840.1050.006
Orientation_Index0.0210.1540.037-0.4160.537-0.179-0.4050.157-0.036-0.4670.191-0.092-0.1000.0981.0000.1700.628-0.4590.278-0.035-0.069-0.2870.2300.287-0.0590.2680.2670.2040.244-0.204-0.089-0.0900.0570.0840.009
Other_Faults0.4130.1150.1310.0970.2170.0710.3300.1580.2480.2470.1880.0920.0540.2330.1701.0000.078-0.1350.206-0.145-0.132-0.0070.1250.178-0.1430.0640.0630.1420.158-0.128-0.056-0.057-0.1360.181-0.005
Outside_Global_Index0.0880.1050.0850.2370.3390.1610.3340.1120.2670.2830.2630.0890.0810.2090.6280.0781.000-0.4230.225-0.046-0.091-0.1140.1630.232-0.0690.0990.0990.1620.196-0.217-0.053-0.0550.0210.0550.011
Outside_X_Index-0.149-0.070-0.276-0.217-0.8050.5480.594-0.2550.7490.9830.591-0.0340.091-0.379-0.459-0.135-0.4231.0000.1300.7480.791-0.1150.078-0.3800.7520.3510.348-0.322-0.3820.8660.0560.0560.6900.103-0.004
Pastry0.1650.0450.0450.1360.2170.1400.1330.1680.1420.1920.1320.1140.0690.1360.2780.2060.2250.1301.000-0.023-0.040-0.1550.0490.105-0.0350.0000.0010.0980.107-0.098-0.027-0.0260.0120.0720.006
Pixels_Areas-0.2180.012-0.380-0.539-0.5920.4320.582-0.1510.9980.7470.905-0.1670.009-0.516-0.035-0.145-0.0460.748-0.0231.0000.961-0.3200.013-0.3170.9670.0820.078-0.306-0.3670.8780.0310.0300.9350.022-0.003
SigmoidOfAreas-0.2200.010-0.368-0.530-0.6330.4920.563-0.1760.9620.7880.898-0.1470.018-0.473-0.069-0.132-0.0910.791-0.0400.9611.000-0.3430.362-0.3110.9410.3700.367-0.300-0.3560.9070.0250.0230.9410.082-0.002
Square_Index0.175-0.1300.1260.3080.082-0.117-0.0490.153-0.320-0.097-0.4060.1010.0930.048-0.287-0.007-0.114-0.115-0.155-0.320-0.3431.0000.1190.002-0.3140.2490.2490.0240.038-0.2910.0350.034-0.4030.0480.007
Stains0.1000.0260.1640.2820.0840.0670.0810.1210.6840.3950.5670.1840.1080.2230.2300.1250.1630.0780.0490.0130.3620.1191.000-0.045-0.2660.1210.1210.0340.039-0.249-0.047-0.045-0.2720.0430.004
Steel_Plate_Thickness0.1250.0340.1800.0520.356-0.158-0.4930.269-0.317-0.353-0.257-0.136-0.1520.1440.2870.1780.232-0.3800.105-0.317-0.3110.002-0.0451.000-0.3320.7170.7150.2290.270-0.330-0.093-0.087-0.2980.2920.008
Sum_of_Luminosity-0.2180.014-0.352-0.524-0.6070.4420.580-0.1650.9680.7480.878-0.0910.076-0.453-0.059-0.143-0.0690.752-0.0350.9670.941-0.314-0.266-0.3321.0000.1710.166-0.315-0.3740.8710.0300.0290.9150.056-0.004
TypeOfSteel_A3000.2460.0790.1900.2320.3190.1930.3750.4700.4320.3740.3990.2290.2300.3850.2680.0640.0990.3510.0000.0820.3700.2490.1210.7170.1711.0000.9980.1630.201-0.3200.0330.033-0.3700.1900.000
TypeOfSteel_A4000.2450.0800.1880.2300.3160.1920.3720.4670.4280.3700.3960.2290.2290.3830.2670.0630.0990.3480.0010.0780.3670.2490.1210.7150.1660.9981.000-0.162-0.1990.317-0.033-0.0330.3670.189-0.001
X_Maximum0.1770.0390.1620.0820.313-0.149-0.3990.234-0.306-0.305-0.271-0.064-0.1200.1900.2040.1420.162-0.3220.098-0.306-0.3000.0240.0340.229-0.3150.163-0.1621.0000.978-0.304-0.022-0.021-0.2970.2690.004
X_Minimum0.1920.0430.1890.0940.367-0.170-0.4740.239-0.366-0.366-0.326-0.048-0.1200.2520.2440.1580.196-0.3820.107-0.367-0.3560.0380.0390.270-0.3740.201-0.1990.9781.000-0.362-0.036-0.035-0.3550.1430.006
X_Perimeter-0.215-0.007-0.350-0.562-0.7450.5760.581-0.1850.8780.8630.793-0.0690.084-0.430-0.204-0.128-0.2170.866-0.0980.8780.907-0.291-0.249-0.3300.871-0.3200.317-0.304-0.3621.0000.0300.0300.8650.0220.000
Y_Maximum0.034-0.006-0.0190.038-0.045-0.0190.106-0.0100.0310.0560.020-0.042-0.049-0.095-0.089-0.056-0.0530.056-0.0270.0310.0250.035-0.047-0.0930.0300.033-0.033-0.022-0.0360.0301.0000.9740.0240.020-0.005
Y_Minimum0.033-0.003-0.0170.039-0.045-0.0210.105-0.0110.0300.0560.019-0.045-0.052-0.094-0.090-0.057-0.0550.056-0.0260.0300.0230.034-0.045-0.0870.0290.033-0.033-0.021-0.0350.0300.9741.0000.0240.020-0.005
Y_Perimeter-0.2500.037-0.391-0.609-0.5730.4510.562-0.1590.9360.6830.962-0.1390.024-0.4840.057-0.1360.0210.6900.0120.9350.941-0.403-0.272-0.2980.915-0.3700.367-0.297-0.3550.8650.0240.0241.0000.083-0.004
Z_Scratch0.1440.0390.1210.0760.0910.0880.1170.1820.1260.1340.0760.0480.0740.1050.0840.1810.0550.1030.0720.0220.0820.0480.0430.2920.0560.1900.1890.2690.1430.0220.0200.0200.0831.0000.003
id0.000-0.003-0.007-0.010-0.001-0.001-0.0000.001-0.004-0.003-0.0060.0060.0010.0060.009-0.0050.011-0.0040.006-0.003-0.0020.0070.0040.008-0.0040.000-0.0010.0040.0060.000-0.005-0.005-0.0040.0031.000

Missing values

2024-03-08T09:02:33.323292image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T09:02:34.026365image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idX_MinimumX_MaximumY_MinimumY_MaximumPixels_AreasX_PerimeterY_PerimeterSum_of_LuminosityMinimum_of_LuminosityMaximum_of_LuminosityLength_of_ConveyerTypeOfSteel_A300TypeOfSteel_A400Steel_Plate_ThicknessEdges_IndexEmpty_IndexSquare_IndexOutside_X_IndexEdges_X_IndexEdges_Y_IndexOutside_Global_IndexLogOfAreasLog_X_IndexLog_Y_IndexOrientation_IndexLuminosity_IndexSigmoidOfAreasPastryZ_ScratchK_ScatchStainsDirtinessBumpsOther_Faults
0058459090997290997716852274113140135801500.73930.40000.50000.00591.00001.00000.01.20410.90310.6990-0.5000-0.01040.14170001000
1180881672835072837243320544447870111168710800.77720.28780.25810.00440.25001.00001.02.63650.77821.73240.7419-0.29970.94910000001
22391922212076221214411388705420131139129141140001400.05570.52820.98950.10770.23630.38570.04.05642.17902.2095-0.0105-0.09441.00000010000
337817893353146335317321016293202114134138701400.72020.33330.33330.00440.37500.93101.02.32220.77821.43140.6667-0.04020.40250010000
4415401560618457618502521726748231821111692013000.12110.53470.08420.01920.21050.98611.02.76941.41501.88080.9158-0.24550.99980000001
551009103389923189930740922264751386118165010700.27610.41360.40910.00530.54541.00001.02.61170.95421.41500.5909-0.18900.87490000001
66596607739072739076020421212247889127137301400.83310.27440.68420.01100.71431.00001.02.30961.17611.32220.3158-0.14970.52120000010
7716731687294065294091571385753142771101692013000.14910.43260.96430.01420.56860.71791.02.75281.38021.75590.0357-0.26610.94081000000
88507521203252203261101251212530124140136001400.72100.48150.33330.01760.80000.75000.02.00431.38020.7782-0.66670.03050.36010000000
998939071341292134129654615705287133168710600.80880.20000.53330.00370.83331.00001.01.73240.69901.17610.4667-0.12280.14000000010
idX_MinimumX_MaximumY_MinimumY_MaximumPixels_AreasX_PerimeterY_PerimeterSum_of_LuminosityMinimum_of_LuminosityMaximum_of_LuminosityLength_of_ConveyerTypeOfSteel_A300TypeOfSteel_A400Steel_Plate_ThicknessEdges_IndexEmpty_IndexSquare_IndexOutside_X_IndexEdges_X_IndexEdges_Y_IndexOutside_Global_IndexLogOfAreasLog_X_IndexLog_Y_IndexOrientation_IndexLuminosity_IndexSigmoidOfAreasPastryZ_ScratchK_ScatchStainsDirtinessBumpsOther_Faults
19209192091065108432578032579615330171683596126136401400.39790.56620.94740.01390.73080.65520.02.18471.27871.1139-0.0526-0.18800.56590000000
1921019210167716893134228313424922615222132179101169210800.00240.37880.73680.00710.80001.00001.02.35411.07921.34240.2632-0.30920.43660000010
19211192111327133212665371266548529145249901181656101000.02510.20000.42860.00440.66671.00001.01.71600.77821.14610.5714-0.20690.17530000001
1921219212411921512277151246712834614374148776327141140201400.05850.48160.37580.10570.26740.42641.04.10842.17032.22530.6242-0.09481.00000010000
19213192130284662945466296494841792001320194191081373012200.00000.48810.06380.01480.23830.98571.03.98151.30102.29670.9362-0.29471.00000000001
192141921474975714321014321917442193122140136000500.89500.15000.85710.00441.00000.80000.01.23050.77820.6021-0.14290.00440.29010001000
192151921572373524885292488541231172627135104133165210700.92430.32540.27780.00650.73330.92161.02.36361.04141.41500.7222-0.09890.53780000001
192161921663115780551578129780114987111241941358012000.01480.43310.22810.01990.18620.95541.02.89211.43141.86920.7719-0.42830.99971000000
19217192179181713172171318412613261480888132169210600.01920.23610.03900.00680.76921.00001.02.10041.04141.41500.9610-0.11620.35090000001
1921819218150515251733458173347118224332278598143168810700.16840.33160.47370.00830.62501.00001.02.26011.14611.50510.5263-0.11200.66190000010